Visualizing spatial data with Python: GeoPandas
\n", "\n", "\n", "> *DS Python for GIS and Geoscience* \n", "> *October, 2020*\n", ">\n", "> *© 2020, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](https://creativecommons.org/licenses/by/4.0/)*\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pandas as pd\n", "import geopandas\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "countries = geopandas.read_file(\"zip://./data/ne_110m_admin_0_countries.zip\")\n", "countries = countries[countries['continent'] != \"Antarctica\"]\n", "cities = geopandas.read_file(\"zip://./data/ne_110m_populated_places.zip\")\n", "rivers = geopandas.read_file(\"zip://./data/ne_50m_rivers_lake_centerlines.zip\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## GeoPandas visualization functionality" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "GeoPandas itself provides some visualization functionality, and together with matplotlib for further customization, you can already get decent results for visualizing vector data." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Basic plot" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "